# devtools::install_github("derekmichaelwright/agData")
library(agData)

# Prep data
myCaption1 <- c("www.dblogr.com/ or derekmichaelwright.github.io/dblogr/ | Data: STATCAN")
myCaption2 <- c("www.dblogr.com/ or derekmichaelwright.github.io/dblogr/ | Data: STATCAN\nNote: most recent years data may be incomplete")
myColors <- c("darkgreen", "darkred", "darkorange", "steelblue", "purple4", "magenta3")
myAreas <- c("Canada", "Quebec", "Ontario", "British Columbia", 
           "Alberta", "Saskatchewan", "Manitoba", "Nova Scotia",
           "Newfoundland and Labrador", "New Brunswick", "Prince Edward Island", 
           "Northwest Territories", "Nunavut", "Yukon")
#
# d1 = Deaths per Week (2010-2023)
mySeasons <- paste(2009:2023, 2010:2024, sep = "-")
myGroups <- c(rep("pre-COVID",10), mySeasons[11:length(mySeasons)])
d1 <- read.csv("1310078301_databaseLoadingData.csv") %>% 
  rename(Date=1, Area=GEO, Value=VALUE) %>%
  mutate(Date = as.Date(Date),
         Year  = as.numeric(substr(Date, 1, 4)),
         Month = as.numeric(substr(Date, 6, 7)),
         Group = ifelse(Year < 2020, "<2020", Year),
         Group = factor(Group, levels = c("<2020", "2020", "2021", "2022", "2023")),
         JulianDay = lubridate::yday(Date),
         AdjJulianDay = ifelse(Month < 8, JulianDay + 365, JulianDay),
         Area = gsub(", place of occurrence", "", Area),
         Area = factor(Area, levels = myAreas)) %>%
    arrange(Date)
# filter incomplete new data
for(i in unique(d1$Area)) {
  mymin <- d1 %>% filter(Area == i, Year < 2020) %>% pull(Value) %>% min()
  d1 <- d1 %>% filter(!(Area == i & Value < mymin))
}
# Calculate Year Group
j <- 1
for(i in 1:nrow(d1)) {
  if(d1$Month[i] < 8) { mySwitch <- T }
  d1$Season[i] <- mySeasons[j]
  d1$SeasonGroup[i] <- myGroups[j]
  if(d1$Month[i] > 7 & mySwitch == T) { j <- j + 1; mySwitch <- F } 
}
d1 <- d1 %>% 
    mutate(Season = factor(Season, levels = mySeasons),
           SeasonGroup = factor(SeasonGroup, levels = unique(myGroups)))
# d2 = Deaths per Week, by gender and age (2010-2023)
d2 <- read.csv("1310076801_databaseLoadingData.csv") %>% 
  rename(Date=1, Age=Age.at.time.of.death, Value=VALUE, Area=GEO) %>%
  mutate(Date = as.Date(Date),
         Age = gsub("Age at time of death, ", "", Age),
         Year  = as.numeric(substr(Date, 1, 4)),
         Month = as.numeric(substr(Date, 6, 7)),
         Group = ifelse(Year < 2020, "<2020", Year),
         Group = factor(Group, levels = c("<2020", "2020", "2021", "2022", "2023")),
         JulianDay = lubridate::yday(Date),
         AdjJulianDay = ifelse(Month < 8, JulianDay + 365, JulianDay),
         Area = gsub(", place of occurrence", "", Area),
         Area = factor(Area, levels = myAreas)) %>%
    arrange(Date)
# Calculate Year Group
j <- 1
for(i in 1:nrow(d2)) {
  if(d2$Month[i] < 8) { mySwitch <- T }
  d2$Season[i] <- mySeasons[j]
  d2$SeasonGroup[i] <- myGroups[j]
  if(d2$Month[i] > 7 & mySwitch == T) { j <- j + 1; mySwitch <- F } 
}
d2 <- d2 %>% 
    mutate(Season = factor(Season, levels = mySeasons),
           SeasonGroup = factor(SeasonGroup, levels = unique(myGroups)))
# d3 = Yearly death rate (1991-2023)
p1 <- read.csv("1710000901_databaseLoadingData.csv") %>% 
  select(Area=GEO, Year=REF_DATE, Population=VALUE) %>%
  filter(Year %in% paste0(1991:2023,"-01")) %>%
  mutate(Year = as.numeric(gsub("-01","",Year)))
  #filter(Month == 1) %>% select(Area, Year, Population=Value)
p2 <- read.csv("1710000501_databaseLoadingData.csv") %>% 
  select(Area=GEO, Year=REF_DATE, Sex, Age.group, Population=VALUE)
yy <- d1 %>% filter(Year > 2020) %>% 
  group_by(Area, Year) %>%
  summarise(Value = sum(Value)) %>%
  mutate(Month.of.death = "Total")
d3 <- read.csv("1310070801_databaseLoadingData.csv") %>%
  rename(Year=1, Area=GEO, Value=VALUE, Unit=UOM) %>%
  mutate(Month.of.death = gsub("Month of death, |, month of death", "", 
                               Month.of.death),
         Area = gsub(", place of residence", "", Area),
         Area = factor(Area, levels = myAreas)) %>%
  filter(Unit == "Number") %>%
  bind_rows(yy) %>%
  rename(Total.Deaths=Value) %>%
  left_join(p1, by = c("Area", "Year")) %>%
  mutate(Death.Rate = 1000 * Total.Deaths / Population) %>%
  filter(Month.of.death == "Total", !is.na(Area)) %>%
  mutate(Group = ifelse(Year < 2020, "<2020", Year),
         Group = factor(Group, levels = c("<2020", "2020", "2021", "2022", "2023")))

# Prep data
xx <- d3 %>% filter(Area == "Canada", Year < 2023)
# Plot
mp1 <- ggplot(xx, aes(x = Year, y = Total.Deaths / 1000, 
                      fill = Group, alpha = Group)) +
  geom_col(color = "black") +
  scale_fill_manual(name = NULL, values = myColors) +
  scale_alpha_manual(name = NULL, values = c(0.4,0.8,0.8,0.8,0.8,0.8)) +
  scale_x_continuous(breaks = c(1991, seq(1995,2020, by = 5))) +
  scale_y_continuous(minor_breaks = seq(0, 400, by = 20)) +
  theme_agData() +
  labs(subtitle = "(A) Total Number of Deaths Per Year in Canada", 
       y = "Thousand Deaths", x = NULL, caption = "")
mp2 <- ggplot(xx, aes(x = Year, y = Death.Rate, 
                      fill = Group, alpha = Group)) +
  geom_col(color = "black") +
  scale_fill_manual(name = NULL, values = myColors) +
  scale_alpha_manual(name = NULL, values = c(0.4,0.8,0.8,0.8,0.8,0.8)) +
  scale_x_continuous(breaks = c(1991, seq(1995,2020, by = 5))) +
  scale_y_continuous(minor_breaks = seq(0, 400, by = 20)) +
  theme_agData() +
  labs(subtitle = "(B) Death Rate Per Year in Canada", 
       y = "Deaths / Thousand", x = NULL, caption = myCaption1)
mp <- ggarrange(mp1, mp2, ncol = 2, legend = "none", common.legend = T)
ggsave("canada_deaths_01_01.png", mp, width = 10, height = 4, bg = "white")

# Create plotting function
ggWeeklyDeaths <- function(area = "Canada", xmin = 2010, xmax = max(d1$Year)) {
  # Prep data
  vv <- as.Date(paste0(as.character(xmin:xmax),"-01-01"))
  xx <- d1 %>% filter(Area == area, Year >= xmin)
  #
  myMax <- max(xx %>% filter(Year < 2020) %>% pull(Value), na.rm = T)
  # Plot
  ggplot(xx, aes(x = Date, y = Value, fill = Group)) +
    geom_col(alpha = 0.7) +
    geom_vline(xintercept = vv, lty = 2, alpha = 0.5) +
    geom_vline(xintercept = as.Date("2020-03-01"), alpha = 0.6) +
    geom_hline(yintercept = myMax, alpha = 0.2) +
    #facet_grid(. ~ Area) +
    scale_fill_manual(values = myColors) +
    scale_x_date(date_breaks = "1 year", date_labels = "%Y", 
                 minor_breaks = "1 year") +
    theme_agData(legend.position = "none") +
    labs(title = area, y = "Weekly Deaths", x = NULL, caption = myCaption2)
}

# Prep data
xx <- d1 %>% mutate(Year = as.numeric(as.character(Year))) %>%
  select(Date, Year, JulianDay, Group, Area, Value) %>%
  arrange(Area, Date) %>%
  spread(Area, Value)
for(i in 5:ncol(xx)) {
  for(k in min(xx$Year):max(xx$Year)) {
    xx[xx$Year == k, i] <- cumsum(xx[xx$Year == k,i])
  }
}
xx <- xx %>% gather(Area, Value, 5:ncol(.)) %>%
  mutate(Area = factor(Area, levels = myAreas)) %>%
  filter(Area %in% myAreas)
# Plot
mp <- ggplot(xx, aes(x = JulianDay, y = Value / 1000, 
                     group = Year, color = Group, alpha = Group)) +
  geom_line() +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 5) +
  scale_color_manual(name = NULL, values = myColors) +
  scale_alpha_manual(name = NULL, values = c(0.5,1,1,1,1)) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Thousand Deaths", x = "Julian Day", caption = myCaption1)
#mp <- ggarrange(mp1, mp2, common.legend = T, legend = "bottom", align = "h")
ggsave("canada_deaths_04_01.png", mp, width = 15, height = 8)

# Create plotting function
ggRespSeasons <- function(myAreas) {
  # Prep data
  xx <- d1 %>% filter(Area %in% myAreas)
  zz <- xx %>% filter(Date == "2020-03-14")
  #
  myBreaks <- c(213, 244, 274, 305, 335, 
                366, 397, 425, 456, 486, 517, 547, 577)
  myLabels <- c("Aug","Sept","Oct","Nov","Dec",
                "Jan","Feb","Mar","Apr", "May","June","July","Aug")
  # Plot
  ggplot(xx, aes(x = AdjJulianDay, y = Value, group = Season, 
                 color = SeasonGroup, alpha = SeasonGroup, size = SeasonGroup)) +
    geom_line() +
    geom_point(data = zz, size = 2, pch = 13, color = "black", alpha = 0.7) +
    facet_wrap(Area ~ ., scales = "free_y", ncol = 5) +
    scale_color_manual(name = NULL, values = myColors) +
    scale_alpha_manual(name = NULL, values = c(0.2,0.8,0.8,0.8,0.8,0.8)) +
    scale_size_manual(name = NULL, values = c(0.5,1,1,1,1,1)) +
    scale_x_continuous(breaks = myBreaks, labels = myLabels) +
    theme_agData(legend.position = "bottom",
                 axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(y = "Deaths Per Week", x = NULL, caption = myCaption2)
}

# Create plotting function
deathPlot3 <- function(myAreas = "Canada") {
  # Prep data
  xx <- d2 %>% filter(Area %in% myAreas, Sex == "Both sexes", Age != "all ages")
  zz <- xx %>% filter(Date == "2020-03-14")
  #
  myBreaks <- c(213, 244, 274, 305, 335, 
                366, 397, 425, 456, 486, 517, 547, 577)
  myLabels <- c("Aug","Sept","Oct","Nov","Dec",
                "Jan","Feb","Mar","Apr", "May","June","July", "Aug")
  # Plot
  ggplot(xx, aes(x = AdjJulianDay, y = Value, group = Season, 
                 color = SeasonGroup, alpha = SeasonGroup, size = SeasonGroup)) +
    geom_line() +
    geom_point(data = zz, size = 1.5, pch = 13, color = "black", alpha = 0.7) +
    facet_grid(Area ~ Age, scales = "free_y") +#labeller = label_wrap_gen(width = 10)
    scale_color_manual(name = NULL, values = myColors) +
    scale_alpha_manual(name = NULL, values = c(0.4,0.8,0.8,0.8,0.8)) +
    scale_size_manual(name = NULL, values = c(0.3,0.75,0.75,0.75,0.75,0.75)) +
    scale_x_continuous(breaks = myBreaks, labels = myLabels) +
    theme_agData(legend.position = "bottom",
                 axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(y = "Deaths Per Week", x = NULL, caption = myCaption2)
}

# Create plotting function
deathPlot5 <- function(myArea = "Canada") {
  # Prep data
  xx <- d2 %>% 
    filter(Area == myArea,
           Sex == "Both sexes", Age != "all ages") %>%
    group_by(Age, Year, Group) %>%
    summarise(Value = sum(Value, na.rm = T))
  # Plot
  ggplot(xx, aes(x = Year, y = Value / 1000, fill = Group, alpha = Group)) +
    geom_col(color = "black") +
    facet_grid(. ~ Age) +
    scale_fill_manual(name = NULL, values = myColors) +
    scale_alpha_manual(name = NULL, values = c(0.4,0.8,0.8,0.8,0.8)) +
    scale_x_continuous(breaks = seq(2010, 2022, 2)) +
    theme_agData(legend.position = "none",
                 axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = myArea, caption = myCaption2,
         y = "Thousand Deaths", x = NULL)
}

ggDeaths65 <- function(myArea = "Canada") {
  # Prep data
  xx <- d2 %>% 
    filter(Area == myArea, Date > "2010-01-01", Sex == "Both sexes",
           Age %in% c("65 to 84 years", "84 years and over"), !is.na(Value)) %>%
    group_by(Area, Year) %>%
    summarise(Value = sum(Value, na.rm = T))
  pi <- p2 %>% 
    filter(Area == myArea, Sex == "Both sexes",
           Age.group %in% c("65 to 69 years", "70 to 74 years", 
                            "75 to 79 years", "80 to 84 years", 
                            "85 to 89 years", "90 to 94 years", 
                            "95 to 99 years", "100 years and over")) %>%
    group_by(Year) %>%
    summarise(Population = sum(Population, na.rm = T))
  xx <- xx %>% left_join(pi, by = "Year") %>%
    mutate(Rate = 100000 * Value / Population)
  # Plot
  ggplot(xx, aes(x = Year, y = Rate)) +
    geom_col(color = "black", fill = "darkred", alpha = 0.7) + 
    scale_fill_manual(values = myColors) +
    scale_x_continuous(breaks = 2010:2022) +
    theme_agData(legend.position = "none",
                 axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = paste(myArea, "- Ages 65 and over"),
         x = NULL, y = "Deaths per 100,000 people", caption = myCaption1)
}

ggDeaths044 <- function(myArea = "Canada") {
  # Prep data
  xx <- d2 %>% filter(Area == myArea, Date > "2010-01-01",
                      Age == "0 to 44 years", Sex == "Both sexes")
  vv <- as.Date(paste0(as.character(2010:2023),"-01-01"))
  # Plot
  ggplot(xx, aes(x = Date, y = Value, fill = Group)) +
    geom_col(alpha = 0.7) +
    geom_vline(xintercept = vv, lty = 2, alpha = 0.5) +
    geom_vline(xintercept = as.Date("2020-03-01"), alpha = 0.25) +
    scale_fill_manual(values = myColors) +
    scale_x_date(date_breaks = "1 year", date_labels = "%Y", 
                 minor_breaks = "1 year") +
    theme_agData(legend.position = "none") +
    labs(title = paste(myArea, "- Ages 0 - 44"),
         x = NULL, y = "Weekly Deaths", caption = myCaption1)
}

# Prep data
d4 <- read.csv("data_canada_deaths.csv") %>%
  gather(Trait, Value, 2:ncol(.)) %>%
  mutate(Value = gsub(",", "", Value),
         Value = as.numeric(Value))
xx <- d4 %>% 
  filter(Year %in% 2020:2021, Trait == "Death.rate..per.1.000.") %>% 
  pull(Value) %>% max(na.rm = T)
xx <- d4 %>% filter(Trait == "Death.rate..per.1.000.") %>%
  mutate(Group = ifelse(Value >= xx, "higher", "Lower"),
         Group = ifelse(Year %in% c(1918, 2020, 2021), "Pandemic", Group))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, alpha = Group)) +
  geom_col(color = "black", fill = "darkred", size = 0.3) +
  scale_x_continuous(breaks = seq(1900, 2020, 20)) +
  scale_fill_manual(values = c("darkred", "darkgreen", "darkred")) +
  scale_alpha_manual(values = c(0.6, 0.3, 0.8)) +
  theme_agData(legend.position = "none") +
  labs(title = "Death Rate in Canada", y = "Deaths per 1000 people", x = NULL,
       caption = "www.dblogr.com/ or derekmichaelwright.github.io/dblogr/ | Data: Wikipedia")
ggsave("canada_deaths_12_01.png", mp, width = 6, height = 4)